Optimal sequential clinical scheduling with no-show

Ji Lin, Purdue University

Abstract

The accessibility and efficiency of outpatient clinic operations are largely affected by appointment schedules. Clinical scheduling is a process of assigning physician appointment times to sequentially calling patients. A significant problem in clinical operations is patient no-show, that is, scheduled patients not showing for their appointments. Overbooking can compensate revenue loss due to no-show, but naive overbooking can result in longer patient waiting times and uneven physician work loads. In the past few years, new overbooking methods have been developed for sequential scheduling that yield higher expected profit than simple scheduling rules, but these often fail to exploit information about the future call-in process, so are of myopic. To fully utilize this important information, we develop a Markov Decision Process (MDP) model for non-myopic sequential clinical scheduling that books patients to optimize the performance of clinic operations. The model is solved by Dynamic Programming (DP) for small problems. Approximate Dynamic Programming (ADP) algorithms based on aggregation and Monte Carlo simulation are developed to find schedules for larger problems. The simulation based approximation shares the value improvement features in ADP and reinforcement learning (RL), but can handle larger state space the traditional methods. A simulated clinic model is developed to verify the effect scheduling methods under complex clinic structure, service, and environment factors. Theoretical computation and simulation results indicate good improvement over myopic methods.

Degree

Ph.D.

Advisors

Lawley, Purdue University.

Subject Area

Health care management|Systems science|Operations research

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